Fuzzy Modeling and Fuzzy Control (Control Engineering) - Hardcover

Zhang, Huaguang; Liu, Derong

Hardcover

ISBN 10: 0817644911 ISBN 13: 9780817644918

Verlag: Birkhäuser, 2006

Inhaltsangabe

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

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Über die Autorin bzw. den Autor

Juan Zhang received the B.S. degree in information and computing science from Shanxi Normal University, Linfen, China, in 2016, and the M.S. degree in basic mathematics and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2018 and 2023, respectively. She is currently a Lecturer with the School of Information Science and Engineering, Northeastern University, where she is also a Post-Doctoral Fellow. Her current research interests include containment control, event-triggered control, formation control, consensus control, fault-tolerant control, DoS attacks for multi-agent systems, and their applications in boiler systems. Huaguang Zhang (Fellow, IEEE) received the B.S. and M.S. degrees in control engineering from Northeast Dianli University, Jilin, China, in 1982 and1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He has authored or coauthored over 280 journals and conference papers, six monographs, and coinvented 90 patents. His current research interests include fuzzy control, stochastic system control, neural networks-based control, nonlinear control, and their applications. He was Recipient of the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003 and the IEEE Transactions on Neural Networks and Learning systems Outstanding Paper Award in 2012. He was named the Cheung Kong Scholar by the Education Ministry of China in 2005. He is E-Letter Chair of the IEEE CIS Society and Former Chair of the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of the IEEE Computational Intelligence Society. He was Associate Editor of the IEEE Transactions on Fuzzy Systems from 2008 to 2013. He is Associate Editor of Automatica, the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Cybernetics, and Neurocomputing. Dongsheng Yang (Senior Member, IEEE) received the B.S. degree in testing technology and instrumentation, the M.S. degree in power electronics and electric drives, and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 1999, 2004, and 2007, respectively. He is currently Professor with Northeastern University. He was supported by the Program for New Century Excellent Talents in University. He has authored or coauthored more than 70 papers published in academic journals and conference proceedings, three monographs, and coinvented 80 patents. His current research interests include distributed generation, multi-energy power systems, and artificial intelligence-based fault diagnosis and protection. He was a recipient of the Second Prize of National Science and Technology Progress. Yanhong Luo (Member, IEEE) received the B.S. degree in automation control, the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2003 and 2009, respectively. From 2016 to 2017, she visited The University of Texas at Arlington as a Visiting Scholar. She is currently a Professor with Northeastern University. She has published more than 100 SCI/EI indexed papers. Her research interests include renewable energy accommodation, distributed control and optimal operation of energy Internet, adaptive dynamic programming, and data-driven self-learning control and its industrial applications. She is a recipient of Andrew P. Sage Best Transactions Paper Award (2015) with IEEE SMC Society. In addition, she is the Former Vice-Chair of Adaptive Dynamic Programming Reinforcement Learning Technical Committee with IEEE Computational Intelligence Society.She served as the Session Chair or a TPC member for more than 20 international conferences. Bowen Zhou (Member, IEEE) received the B.Sc. and M.Sc. degrees in electrical engineering from Wuhan University,

Von der hinteren Coverseite

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems.

Based on three types of fuzzy models―the Mamdani fuzzy model, the Takagi–Sugeno fuzzy model, and the fuzzy hyperbolic model―the book addresses a number of important issues in fuzzy control systems, including fuzzy modeling, fuzzy inference, stability analysis, systematic design frameworks, robustness, and optimality. The authors develop several advanced control schemes, such as the fuzzy model-based generalized predictive control scheme, the fuzzy adaptive control scheme based on fuzzy basis function vectors, the fuzzy control scheme based on fuzzy performance evaluators, and the fuzzy sliding-mode control scheme. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

Details zur Ausgabe

• Verlag: Birkhäuser
• Erscheinungsdatum: 2006
• Sprache: Englisch
• ISBN 10: 0817644911
• ISBN 13: 9780817644918
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 432
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland